依据模糊均值聚类故障检测原理，对舰船电气系统初始状态数据样本归一化后，构建舰船电气系统状态数据模糊相似矩阵，依据该矩阵分析系统状态数据亲疏度；利用亲疏度结果获取状态数据的最佳权值指数，并对状态数据样本进行加权处理，降低系统状态数据聚类稀疏度；将最佳权值指数赋予到状态数据中，同时更新模糊C-均值聚类目标函数。通过设置聚类类别数量、停止阈值和迭代次数后，求解更新模糊C-均值聚类目标函数并输出聚类隶属度和聚类中心，完成舰船电气系统初始状态数据聚类，得到舰船电气系统故障类别。实验结果表明，模糊C-均值聚类钻粉在舰船电气系统故障类型检测过程中具备较低的稀疏度，可有效检测舰船电气系统故障类型的同时其检测结果贴近度数值最高接近1.0。
Based on the fault detection principle of fuzzy C-means clustering, the initial state data samples of ship electrical system were normalized, and the fuzzy similarity matrix of ship electrical system state data was constructed, and the affinity degree of system state data was analyzed according to the matrix. The optimal weight index of the state data was obtained by using the results of affinity and affinity, and the state data samples were weighted to reduce the clustering sparsity of the system state data. The optimal weight index was assigned to the state data, and the fuzzy C-mean clustering objective function was updated. After setting the number of clustering categories, stop threshold and iteration times, the fuzzy C-mean clustering objective function was solved and updated, and the clustering membership degree and clustering center were output. The initial state data clustering of ship electrical system was completed, and the fault categories of ship electrical system were obtained. The experimental results show that the fuzzy C-means clustering drill powder has a low sparsity in the detection process of warship electrical system fault type, and can effectively detect the warship electrical system fault type, while the highest value of the detection result proximity is close to 1.0.
2022,44(4): 156-160 收稿日期：2021-09-25
DOI：10.3404/j.issn.1672-7649.2022.04.033
分类号：TP391
作者简介：林航（1984 ? ），男，工程师，研究方向为船舶电气
参考文献：
[1] 王印松, 商丹丹, 王艳飞, 等. 改进的模糊聚类在控制系统故障诊断中的应用[J]. 计算机工程与科学, 2018, 40(2): 326–330
[2] 邢瑞康, 李成海. 改进的聚类算法在入侵检测系统中的应用[J]. 火力与指挥控制, 2019, 44(2): 124–128
[3] 史丽萍, 宋朝鹏, 李明泽, 等. 基于SAAFSA优化加权模糊聚类算法的变压器故障诊断[J]. 电测与仪表, 2018, 55(11): 12–18
[4] 杨帆, 金鑫, 沈煜, 等. 小电流接地故障多层次分类及其识别模型[J]. 电力系统自动化, 2018, 42(15): 186–191
[5] 郑丽丽, 王志国, 刘飞. 模糊聚类下气动阀门粘滞检测方法的改进[J]. 机械科学与技术, 2018, 37(2): 300–305
[6] 宿强, 杨景玉, 王阳萍. 基于直觉模糊C核均值聚类算法的合成孔径雷达图像变化检测[J]. 激光与光电子学进展, 2019, 56(19): 270–277
[7] 王玲, 徐培培. 基于自适应增量学习的时间序列模糊聚类算法[J]. 电子学报, 2019, 47(5): 983–991
[8] 杜明洋, 毕大平, 王树亮, 等. 基于模糊聚类的群分离与合并跟踪算法[J]. 探测与控制学报, 2018, 40(6): 121–127
[9] HU Hong-qian, SHI Wei-feng, LAN Ying, et al. Design of dynamic power quality monitoring and fault diagnosis system of ship- power system based on Ethernet[J]. Chinese Journal of Ship Research, 2018, 13(1): 120–126
[10] 贾鑫, 张惊雷, 温显斌. 双监督信号深度学习的电气设备红外故障识别[J]. 红外与激光工程, 2018, 47(07): 32–38
[11] 刘齐, 王茂军, 高强, 等. 基于红外成像技术的电气设备故障检测[J]. 电测与仪表, 2019, 56(10): 122–126+152
[12] ZHOU Zheng-qin, FENG Zhen-xin, ZHOU Dong-guo, et al. Fault region extraction of electrical equipment in infrared image by using an extended mean shift method[J]. Infrared Technology, 2019, 41(1): 78–83
[13] 谭阳红, 蒋鹏, 罗研彬, 等. 基于电流动态偏差值的MMC-MTDC直流侧故障识别[J]. 电力系统及其自动化学报, 2019, 31(11): 1–9
[14] ZHANG Hao-yu,YAO Gang,YIN Zhi-zhu, et al. Faul tstate perception method for six-phase PMSM based on wavelet neural network and KNN machine learning algorithm[J]. Electrical Measurement & Instrumentation, 2019, 56(2): 1–9
[15] 刘鑫, 李珉, 郭锐强. 基于宽频特性测量的电气设备局部放电检测[J]. 计算机仿真, 2020, 37(5): 379–383
